We’re currently working on a project to migrate a customer from AWS to Azure. As always, we like to put a Continuous Delivery pipeline at the heart of the project to ensure there is zero friction pushing out changes across all dev, test and production environments.

To make it easy to use, I wrapped the Release Annotations PowerShell Script with a TeamCity MetaRunner which allows you to pass in values from the build server (build number, user who triggered deployment, git hash etc) into the script:

By adding the Release Annotation as a build step, Application Insights is automatically notified every time a deployment occurs. To view the Release Annotation, click the “Metrics Explorer” button and the annotation should be visible as a blue circle with an “i” in the centre. If you click this icon, the Release Annotation blade appears which contains the values passed in by TeamCity.

The data captured in the Release Annotation is just a property bag, and can be easily customised; simply add more fields into the MetaRunner configuration screen and add these to the $releaseProperties array and the values will pushed through to Application Insights.

It’s been great to see Microsoft embracing the R language on Azure, being able to easily operationalize R assets is changing the way organisations think about their analytical workloads.

While it is trivial to publish an R model as a web service in Azure Machine Learning, there is still no easy way to integrate this within standard ALM processes and tooling. For one of our customers this was a big issue. They use Visual Studio Team Services (VSTS) and wanted a solution that would allow them to version their R models and deploy them across dev, test and production environments.

We recently solved this problem by creating a set of reusable scripts, designed to be plugged in to VSTS or any similar automated deployment tooling, that can deploy R models to Azure ML.

The video below shows the end to end developer experience in action. In the video we use the recently released R Tools for Visual Studio, but you use any R development environment such as R Studio.

Those of you dabbling around with/in Azure Data Lake and Visual Studio will be aware that it is possible to run U-SQL scripts locally on your development machine. This is useful when developing and debugging scripts against small sets of data since you do not incur the overhead of submitting and running jobs up on Azure.

U-SQL can access data files stored locally on your development machine, however, Microsoft have recently changed the location of the local root data folder. This will effect anyone who installed Visual Studio Tools for Data Lake prior to 12th December 2015.

For version 2.0.3000.0 the root data folder is:c:\LocalRunDataRoot

And for versions greater than 2.0.4000.0 the folder you need is:%UserProfile%\AppData\Local\USQLDataRoot